Relevant bibliographies by topics / Image-level Supervision

Academic literature on the topic 'Image-level Supervision'

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Published: 6 September 2023

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Journal articles on the topic "Image-level Supervision"

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Ge, Ce, Jingyu Wang, Qi Qi, Haifeng Sun, Tong Xu, and Jianxin Liao. "Scene-Level Sketch-Based Image Retrieval with Minimal Pairwise Supervision." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 650–57. http://dx.doi.org/10.1609/aaai.v37i1.25141.

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The sketch-based image retrieval (SBIR) task has long been researched at the instance level, where both query sketches and candidate images are assumed to contain only one dominant object. This strong assumption constrains its application, especially with the increasingly popular intelligent terminals and human-computer interaction technology. In this work, a more general scene-level SBIR task is explored, where sketches and images can both contain multiple object instances. The new general task is extremely challenging due to several factors: (i) scene-level SBIR inherently shares sketch-specific difficulties with instance-level SBIR (e.g., sparsity, abstractness, and diversity), (ii) the cross-modal similarity is measured between two partially aligned domains (i.e., not all objects in images are drawn in scene sketches), and (iii) besides instance-level visual similarity, a more complex multi-dimensional scene-level feature matching problem is imposed (including appearance, semantics, layout, etc.). Addressing these challenges, a novel Conditional Graph Autoencoder model is proposed to deal with scene-level sketch-images retrieval. More importantly, the model can be trained with only pairwise supervision, which distinguishes our study from others in that elaborate instance-level annotations (for example, bounding boxes) are no longer required. Extensive experiments confirm the ability of our model to robustly retrieve multiple related objects at the scene level and exhibit superior performance beyond strong competitors.
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Zhou, Hongming, Kang Song, Xianglei Zhang, Wenyong Gui, and Qiusuo Qian. "WAILS: Watershed Algorithm With Image-Level Supervision for Weakly Supervised Semantic Segmentation." IEEE Access 7 (2019): 42745–56. http://dx.doi.org/10.1109/access.2019.2908216.

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Zhang, Xiu. "Superresolution Reconstruction of Remote Sensing Image Based on Middle-Level Supervised Convolutional Neural Network." Journal of Sensors 2022 (January 4, 2022): 1–14. http://dx.doi.org/10.1155/2022/2603939.

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Image has become one of the important carriers of visual information because of its large amount of information, easy to spread and store, and strong sense of sense. At the same time, the quality of image is also related to the completeness and accuracy of information transmission. This research mainly discusses the superresolution reconstruction of remote sensing images based on the middle layer supervised convolutional neural network. This paper designs a convolutional neural network with middle layer supervision. There are 16 layers in total, and the seventh layer is designed as an intermediate supervision layer. At present, there are many researches on traditional superresolution reconstruction algorithms and convolutional neural networks, but there are few researches that combine the two together. Convolutional neural network can obtain the high-frequency features of the image and strengthen the detailed information; so, it is necessary to study its application in image reconstruction. This article will separately describe the current research status of image superresolution reconstruction and convolutional neural networks. The middle supervision layer defines the error function of the supervision layer, which is used to optimize the error back propagation mechanism of the convolutional neural network to improve the disappearance of the gradient of the deep convolutional neural network. The algorithm training is mainly divided into four stages: the original remote sensing image preprocessing, the remote sensing image temporal feature extraction stage, the remote sensing image spatial feature extraction stage, and the remote sensing image reconstruction output layer. The last layer of the network draws on the single-frame remote sensing image SRCNN algorithm. The output layer overlaps and adds the remote sensing images of the previous layer, averages the overlapped blocks, eliminates the block effect, and finally obtains high-resolution remote sensing images, which is also equivalent to filter operation. In order to allow users to compare the superresolution effect of remote sensing images more clearly, this paper uses the Qt5 interface library to implement the user interface of the remote sensing image superresolution software platform and uses the intermediate layer convolutional neural network and the remote sensing image superresolution reconstruction algorithm proposed in this paper. When the training epoch reaches 35 times, the network has converged. At this time, the loss function converges to 0.017, and the cumulative time is about 8 hours. This research helps to improve the visual effects of remote sensing images.
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Cha, Junuk, Muhammad Saqlain, Changhwa Lee, Seongyeong Lee, Seungeun Lee, Donguk Kim, Won-Hee Park, and Seungryul Baek. "Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation." Applied Sciences 11, no. 20 (October 18, 2021): 9724. http://dx.doi.org/10.3390/app11209724.

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Three-dimensional human pose and shape estimation is an important problem in the computer vision community, with numerous applications such as augmented reality, virtual reality, human computer interaction, and so on. However, training accurate 3D human pose and shape estimators based on deep learning approaches requires a large number of images and corresponding 3D ground-truth pose pairs, which are costly to collect. To relieve this constraint, various types of weakly or self-supervised pose estimation approaches have been proposed. Nevertheless, these methods still involve supervision signals, which require effort to collect, such as unpaired large-scale 3D ground truth data, a small subset of 3D labeled data, video priors, and so on. Often, they require installing equipment such as a calibrated multi-camera system to acquire strong multi-view priors. In this paper, we propose a self-supervised learning framework for 3D human pose and shape estimation that does not require other forms of supervision signals while using only single 2D images. Our framework inputs single 2D images, estimates human 3D meshes in the intermediate layers, and is trained to solve four types of self-supervision tasks (i.e., three image manipulation tasks and one neural rendering task) whose ground-truths are all based on the single 2D images themselves. Through experiments, we demonstrate the effectiveness of our approach on 3D human pose benchmark datasets (i.e., Human3.6M, 3DPW, and LSP), where we present the new state-of-the-art among weakly/self-supervised methods.
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Han, Sujy, Tae Bok Lee, and Yong Seok Heo. "Deep Image Prior for Super Resolution of Noisy Image." Electronics 10, no. 16 (August 20, 2021): 2014. http://dx.doi.org/10.3390/electronics10162014.

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Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without data-driven training such that it can be used for various image restoration problems. However, super-resolution tasks are difficult to perform with DIP when the target image is noisy. The super-resolved image becomes noisy because the reconstruction loss of DIP does not consider the noise in the target image. Furthermore, when the target image contains noise, the optimization process of DIP becomes unstable and sensitive to noise. In this paper, we propose a noise-robust and stable framework based on DIP. To this end, we propose a noise-estimation method using the generative adversarial network (GAN) and self-supervision loss (SSL). We show that a generator of DIP can learn the distribution of noise in the target image with the proposed framework. Moreover, we argue that the optimization process of DIP is stabilized when the proposed self-supervision loss is incorporated. The experiments show that the proposed method quantitatively and qualitatively outperforms existing single image super-resolution methods for noisy images.
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Qin, Jie, Jie Wu, Xuefeng Xiao, Lujun Li, and Xingang Wang. "Activation Modulation and Recalibration Scheme for Weakly Supervised Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 2117–25. http://dx.doi.org/10.1609/aaai.v36i2.20108.

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Image-level weakly supervised semantic segmentation (WSSS) is a fundamental yet challenging computer vision task facilitating scene understanding and automatic driving. Most existing methods resort to classification-based Class Activation Maps (CAMs) to play as the initial pseudo labels, which tend to focus on the discriminative image regions and lack customized characteristics for the segmentation task. To alleviate this issue, we propose a novel activation modulation and recalibration (AMR) scheme, which leverages a spotlight branch and a compensation branch to obtain weighted CAMs that can provide recalibration supervision and task-specific concepts. Specifically, an attention modulation module (AMM) is employed to rearrange the distribution of feature importance from the channel-spatial sequential perspective, which helps to explicitly model channel-wise interdependencies and spatial encodings to adaptively modulate segmentation-oriented activation responses. Furthermore, we introduce a cross pseudo supervision for dual branches, which can be regarded as a semantic similar regularization to mutually refine two branches. Extensive experiments show that AMR establishes a new state-of-the-art performance on the PASCAL VOC 2012 dataset, surpassing not only current methods trained with the image-level of supervision but also some methods relying on stronger supervision, such as saliency label. Experiments also reveal that our scheme is plug-and-play and can be incorporated with other approaches to boost their performance. Our code is available at: https://github.com/jieqin-ai/AMR.
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Bonfiglio, Basilio. "Ricostruzione della storia del paziente. Supervisione psicoanalitica in psichiatria." PSICOBIETTIVO, no. 3 (October 2009): 77–89. http://dx.doi.org/10.3280/psob2008-003007.

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- It is taken into account one of the functions of the psychoanalytic group clinical thinking within Mental Health Services: the collection-reconstruction of the patient's history. This work, especially in rehabilitation centres where the patient stays for long term periods, allows to relocate the problems and tensions arising from dealing with the patient within a context which grants their comprehension on a deep emotional and thinking level. From that stems an ongoing redefinition of the patient's image and his/her better individualization in the mind of those who look after them. This in turn fosters a consolidation of their own identity as Mental Health professionals. A transcript of two supervision meetings with the staff of a exemplifies some aspects of this work.Key Words: Supervision, Patient's History, Psychosis, Identity, Projective Identification, Rehabilitation, Residential Therapeutic Centre.Parole chiave: supervisione, storia del paziente, psicosi, identitŕ, identificazione proiettiva, riabilitazione, strutture intermedie.
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Li, Yanyan, Weilong Peng, Keke Tang, and Meie Fang. "Spatio-Frequency Decoupled Weak-Supervision for Face Reconstruction." Computational Intelligence and Neuroscience 2022 (September 22, 2022): 1–12. http://dx.doi.org/10.1155/2022/5903514.

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3D face reconstruction has witnessed considerable progress in recovering 3D face shapes and textures from in-the-wild images. However, due to a lack of texture detail information, the reconstructed shape and texture based on deep learning could not be used to re-render a photorealistic facial image since it does not work in harmony with weak supervision only from the spatial domain. In the paper, we propose a method of spatio-frequency decoupled weak-supervision for face reconstruction, which applies the losses from not only the spatial domain but also the frequency domain to learn the reconstruction process that approaches photorealistic effect based on the output shape and texture. In detail, the spatial domain losses cover image-level and perceptual-level supervision. Moreover, the frequency domain information is separated from the input and rendered images, respectively, and is then used to build the frequency-based loss. In particular, we devise a spectrum-wise weighted Wing loss to implement balanced attention on different spectrums. Through the spatio-frequency decoupled weak-supervision, the reconstruction process can be learned in harmony and generate detailed texture and high-quality shape only with labels of landmarks. The experiments on several benchmarks show that our method can generate high-quality results and outperform state-of-the-art methods in qualitative and quantitative comparisons.
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Gupta, Arjun, Zengming Shen, and Thomas Huang. "Text Embedding Bank for Detailed Image Paragraph Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15791–92. http://dx.doi.org/10.1609/aaai.v35i18.17892.

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Existing deep learning-based models for image captioning typically consist of an image encoder to extract visual features and a language model decoder, an architecture that has shown promising results in single high-level sentence generation. However, only the word-level guiding signal is available when the image encoder is optimized to extract visual features. The inconsistency between the parallel extraction of visual features and sequential text supervision limits its success when the length of the generated text is long (more than 50 words). We propose a new module, called the Text Embedding Bank (TEB), to address this problem for image paragraph captioning. This module uses the paragraph vector model to learn fixed-length feature representations from a variable-length paragraph. We refer to the fixed-length feature as the TEB. This TEB module plays two roles to benefit paragraph captioning performance. First, it acts as a form of global and coherent deep supervision to regularize visual feature extraction in the image encoder. Second, it acts as a distributed memory to provide features of the whole paragraph to the language model, which alleviates the long-term dependency problem. Adding this module to two existing state-of-the-art methods achieves a new state-of-the-art result on the paragraph captioning Stanford Visual Genome dataset.
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Xiao, Shuomin, Aiping Qu, Penghui He, and Han Hong. "CA-Net: Context Aggregation Network for Nuclei Classification in Histopathology Image." Journal of Physics: Conference Series 2504, no. 1 (May 1, 2023): 012031. http://dx.doi.org/10.1088/1742-6596/2504/1/012031.

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Abstract Accurately classifying nuclei in histopathology images is essential for cancer diagnosis and prognosis. However, due to the touching nuclei, nucleus shape variation, background complexity, and image artifacts, end-to-end nucleus classification is still difficult and challenging. In this manuscript, we propose a context aggregation network (CA-Net) for nuclei classification by fusing global contextual information which is critical for classifying nuclei in histopathology images. Specifically, we propose a multi-level semantic supervision (MSS) module focusing on extracting multi-scale context information by varying three different kernel sizes, and dynamically aggregating the context information from high to low level. Furthermore, we employ the GPG and SAPF modules in encoder and decoder networks to exact and aggregate global context information. Finally, the proposed network is verified on a mainstream nuclei classification image datasets (PanNuke) and achieves an improved global accuracy of 0.816. Our proposed MSS module can be easily transferred into any UNet-liked architecture as a deep supervision mechanism.
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Dissertations / Theses on the topic "Image-level Supervision"

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Pandey, Gaurav. "Deep Learning with Minimal Supervision." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4315.

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Abstract In recent years, deep neural networks have achieved extraordinary performance on supervised learning tasks. Convolutional neural networks (CNN) have vastly improved the state of the art for most computer vision tasks including object recognition and segmentation. However, their success relies on the presence of a large amount of labeled data. In contrast, relatively fewer work has been done in deep learning to handle scenarios when access to ground truth is limited, partial or completely absent. In this thesis, we propose models to handle challenging problems with limited labeled information. Our first contribution is a neural architecture that allows for the extraction of infinitely many features from an object while allowing for tractable inference. This is achieved by using the `kernel trick', that is, we express the inner product in the infinite dimensional feature space as a kernel. The kernel can either be computed exactly for single layer feedforward networks, or approximated by an iterative algorithm for deep convolutional networks. The corresponding models are referred to as stretched deep networks (SDN). We show that when the amount of training data is limited, SDNs with random weights drastically outperform fully supervised CNNs with similar architectures. While SDNs perform reasonably well for classification with limited labeled data, they can not utilize unlabeled data which is often much easier to obtain. A common approach to utilize unlabeled data is to couple the classifier with an autoencoder (or its variants) thereby minimizing reconstruction error in addition to the classification error. We discuss the limitations of decoder based architectures and propose a model that allows for the utilization of unlabeled data without the need of a decoder. This is achieved by jointly modeling the distribution of data and latent features in a manner that explicitly assigns zero probability to unobserved data. The joint probability of the data and the latent features is maximized using a two-step EM-like procedure. Depending on the task, we allow the latent features to be one-hot or real-valued vectors and define a suitable prior on the features. For instance, one-hot features correspond to class labels and are directly used for the unsupervised and semi-supervised classification tasks. For real-valued features, we use hierarchical Bayesian models as priors over the latent features. Hence, the proposed model, which we refer to as discriminative encoder (or DisCoder), is flexible in the type of latent features that it can capture. The proposed model achieves state-of-the-art performance on several challenging datasets. Having addressed the problem of utilizing unlabeled data for classification, we move to a domain where obtaining labels is a lot more expensive, that is, semantic segmentation of images. Explicitly labeling each pixel of an image with the object that the pixel belongs to, is an expensive operation, in terms of time as well as effort? Currently, only a few classes of images have been densely (pixel-level) labeled. Even among these classes, only a few images per class have pixel-level supervision. Models that rely on densely-labeled images, cannot utilize a much larger set of weakly annotated images available on the web. Moreover, these models cannot learn the segmentation masks for new classes, where there is no densely labeled data. Hence, we propose a model for utilizing weakly-labeled data for semantic segmentation of images. This is achieved by generating fake labels for each image, while simultaneously forcing the output of the CNN to satisfy the mean-field constraints imposed by a conditional random field. We show that one can enforce the CRF constraints by forcing the distribution at each pixel to be close to the distribution of its neighbors. The proposed model is very fast to train and achieves state-of-the-art performance on the popular VOC-2012 dataset for the task of weakly supervised semantic segmentation of images.
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Book chapters on the topic "Image-level Supervision"

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Zhou, Xingyi, Rohit Girdhar, Armand Joulin, Philipp Krähenbühl, and Ishan Misra. "Detecting Twenty-Thousand Classes Using Image-Level Supervision." In Lecture Notes in Computer Science, 350–68. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20077-9_21.

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Qi, Xiaojuan, Zhengzhe Liu, Jianping Shi, Hengshuang Zhao, and Jiaya Jia. "Augmented Feedback in Semantic Segmentation Under Image Level Supervision." In Computer Vision – ECCV 2016, 90–105. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46484-8_6.

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Wu, Zhuoyue, Hansheng Li, Lei Cui, Yuxin Kang, Jianye Liu, Haider Ali, Jun Feng, and Lin Yang. "Interpretable Histopathology Image Diagnosis via Whole Tissue Slide Level Supervision." In Machine Learning in Medical Imaging, 40–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87589-3_5.

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Marcos, Diego, Jana Kierdorf, Ted Cheeseman, Devis Tuia, and Ribana Roscher. "A Whale’s Tail - Finding the Right Whale in an Uncertain World." In xxAI - Beyond Explainable AI, 297–313. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_15.

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AbstractExplainable machine learning and uncertainty quantification have emerged as promising approaches to check the suitability and understand the decision process of a data-driven model, to learn new insights from data, but also to get more information about the quality of a specific observation. In particular, heatmapping techniques that indicate the sensitivity of image regions are routinely used in image analysis and interpretation. In this paper, we consider a landmark-based approach to generate heatmaps that help derive sensitivity and uncertainty information for an application in marine science to support the monitoring of whales. Single whale identification is important to monitor the migration of whales, to avoid double counting of individuals and to reach more accurate population estimates. Here, we specifically explore the use of fluke landmarks learned as attention maps for local feature extraction and without other supervision than the whale IDs. These individual fluke landmarks are then used jointly to predict the whale ID. With this model, we use several techniques to estimate the sensitivity and uncertainty as a function of the consensus level and stability of localisation among the landmarks. For our experiments, we use images of humpback whale flukes provided by the Kaggle Challenge “Humpback Whale Identification” and compare our results to those of a whale expert.
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Ennser-Kananen, Johanna, Erja Kilpeläinen, Taina Saarinen, and Heidi Vaarala. "Language Education for Everyone? Busting Access Myths." In Finland’s Famous Education System, 351–67. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8241-5_22.

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AbstractFinland has, rather successfully, promoted an image of itself as a model of educational excellence and linguistic equity. This chapter problematises this image by analysing Finnish language education policies at the comprehensive school level. For our analysis we use a three-fold understanding of access as; (a) having the opportunity to participate in language education (getting in); (b) participating in education that is meaningful and effective for the pupil (getting it); and (c) receiving credentials that are societally legitimate and valuable assets (getting out). We elaborate on each aspect of access by debunking three myths for the Finnish context that: (a) Multilingualism is politically valued; (b) the curriculum promotes multilingual education; and (c) the education system offers equal opportunities to all, regardless of language. We conclude with a mixed picture. While initiatives have been put in place to expand participation in language learning and develop multilingual pedagogies, the societal status of national languages and constitutional bilingualism have also, somewhat paradoxically, strengthened monolingual ideologies. Such ideologies have contributed to the erasure of Indigenous and autochthonous languages from education and minimise the position of allochthonous (migrant) languages in curriculum and education. We propose several reforms in teacher education and a more systematic, long term, national supervision of (language) education policy in the service of equitable multilingual education.
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Jung, Wonsik, Da-Woon Heo, Eunjin Jeon, Jaein Lee, and Heung-Il Suk. "Inter-regional High-Level Relation Learning from Functional Connectivity via Self-supervision." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 284–93. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87196-3_27.

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Rot, Peter, Peter Peer, and Vitomir Štruc. "Detecting Soft-Biometric Privacy Enhancement." In Handbook of Digital Face Manipulation and Detection, 391–411. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7_18.

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AbstractWith the proliferation of facial analytics and automatic recognition technology that can automatically extract a broad range of attributes from facial images, so-called soft-biometric privacy-enhancing techniques have seen increased interest from the computer vision community recently. Such techniques aim to suppress information on certain soft-biometric attributes (e.g., age, gender, ethnicity) in facial images and make unsolicited processing of the facial data infeasible. However, because the level of privacy protection ensured by these methods depends to a significant extent on the fact that privacy-enhanced images are processed in the same way as non-tampered images (and not treated differently), it is critical to understand whether privacy-enhancing manipulations can be detected automatically. To explore this issue, we design a novel approach for the detection of privacy-enhanced images in this chapter and study its performance with facial images processed by three recent privacy models. The proposed detection approach is based on a dedicated attribute recovery procedure that first tries to restore suppressed soft-biometric information and based on the result of the restoration procedure then infers whether a given probe image is privacy enhanced or not. It exploits the fact that a selected attribute classifier generates different attribute predictions when applied to the privacy-enhanced and attribute-recovered facial images. This prediction mismatch (PREM) is, therefore, used as a measure of privacy enhancement. In extensive experiments with three popular face datasets we show that the proposed PREM model is able to accurately detect privacy enhancement in facial images despite the fact that the technique requires no supervision, i.e., no examples of privacy-enhanced images are needed for training.
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Erdos, David. "Second-Generation European Data Protection and Professional Journalism." In European Data Protection Regulation, Journalism, and Traditional Publishers, 70–99. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841982.003.0005.

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This chapter explores the legislative interface between data protection and the professional journalistic media under the Data Protection Directive (DPD) and then examines the formal regulatory guidance produced by European Data Protection Authorities (DPAs) during the same period. Despite the DPD’s emphasis on ensuring a careful balancing between equally fundamental rights, statutory provisions at State level were profoundly divergent. In broad terms, Northern European States tended to grant journalism sweeping exemptions from data protection, whilst Southern and Eastern European States set down tough standards even in this sensitive area. These media system differences mapped on to broader cultural fissures concerning individualism, uncertainty avoidance, and attitudes towards power inequalities. In the great majority of cases the national DPA retained a supervisory role in this area and over 60 per cent of these bodies, as well as the Article 29 Working Party, had published some statutory guidance. However, this guidance was often confined to a brief elucidation of the importance of contextual rights balancing coupled, in a number of cases, with an emphasis on promoting a co-regulatory connection between statutory supervision and self-regulation. A minority of DPAs did produce much more extensive guidance focusing especially on children’s rights over data, image rights and visual/audio-visual content, and the right to be forgotten and digital news/media archives.
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Solanke, Iyiola. "Introduction." In On Crime, Society, and Responsibility in the work of Nicola Lacey, 1–8. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198852681.003.0001.

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I had the pleasure and privilege to be guided by Nicola Lacey during my doctorate in the Law Department at the London School of Economics. My supervisor became my friend and over the years a mentor, a sponsor and an ally. Together with my co-supervisor, Damian Chalmers—who introduced us—she not only helped me to excavate und unwind the entangled relationship between law, politics, and society but also to navigate academia, a space that can be both lonely and precarious for a black woman. At both a personal and professional level, she is an excellent role model and image of who and what an academic can be. One contributor put it well:...
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"there are remarks about the cost per unit of raw soda. The direc­ tors were well aware that production level and cost per unit were in inverse ratio: . . . this year we produced 448 000 d: more than the pre­ ceding year; therefore, the overheads for salaries and in­ terests contribute to the cost per unit proportionately less. Allocation of overhead. The allocation of overhead costs was discussed during four meetings of the Board of Directors: March 7 and 13, 1832; August 20, 1833; September 4, 1834.10 The members of the Board discussed the allocation of overheads between glass and chemical products. At the first meeting, on March 7, 1832, it was reported: The Administration (of the Company) has decided that the overheads accounts of every branch will be divided in accordance with the production as shown on the books; each product {produits speciaux) will be charged with its own direct expenses (frais speciaux). At the meeting the next week (March 13, 1832), the record indi­ cates that overhead cost allocation was again discussed: It has been pointed out to the Board of Directors by one of the members that the preceding decree, dividing over­ head expenses in accordance with each factory's produc­ tion stated by its books, could entail serious drawbacks; for example, in a year of very low sales, it we stop the production and only sell glass in stock, we should be obliged to make the chemical products bear all the over­ head expenses, which means a considerable increase in their cost prices and gives us a wrong image of them. He (the member of the B. of D.) thinks it much more conve­ nient to divide the overhead expenses in accordance with the fixed capital involved in each one of the two factories, as shown by the general inventory, capital to which we add the required working capital; with such a manner of distribution, each factory would bear its own part of overheads required by the supervision and administration of its capital. In the above-mentioned case of a factory's producing next to nothing, we would have to state a loss for that factory, which is quite normal." In Accounting in France (RLE Accounting), 260. Routledge, 2014. http://dx.doi.org/10.4324/9781315871042-28.

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Conference papers on the topic "Image-level Supervision"

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Pandey, Gaurav, and Ambedkar Dukkipati. "Learning to Segment With Image-Level Supervision." In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2019. http://dx.doi.org/10.1109/wacv.2019.00202.

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Cholakkal, Hisham, Guolei Sun, Fahad Shahbaz Khan, and Ling Shao. "Object Counting and Instance Segmentation With Image-Level Supervision." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01268.

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Wang, Lijun, Huchuan Lu, Yifan Wang, Mengyang Feng, Dong Wang, Baocai Yin, and Xiang Ruan. "Learning to Detect Salient Objects with Image-Level Supervision." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.404.

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Liu, Jiawei, Jing Zhang, Yicong Hong, and Nick Barnes. "Learning structure-aware semantic segmentation with image-level supervision." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533846.

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Ahn, Jiwoon, and Suha Kwak. "Learning Pixel-Level Semantic Affinity with Image-Level Supervision for Weakly Supervised Semantic Segmentation." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00523.

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Ling, Hefei, Junrui Huang, Chengxin Zhao, Yutong Yao, Jiazhong Chen, and Ping Li. "Learning Diverse Local Patterns for Deepfake Detection with Image-level Supervision." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533912.

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Yao, Haojiang, Ya-Li Hou, Rui Wang, Xiaoli Hao, and Zhijiang Hou. "An Improved IRNet for Instance Segmentation Based on Image-level Supervision." In 2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2022. http://dx.doi.org/10.1109/cisp-bmei56279.2022.9979845.

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Kansal, Priya, and Sabari Nathan. "A Multi-Level Supervision Model: A novel approach for Thermal Image Super Resolution." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00055.

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Fan, Zhihao, Zhongyu Wei, Zejun Li, Siyuan Wang, Haijun Shan, Xuanjing Huang, and Jianqing Fan. "Constructing Phrase-level Semantic Labels to Form Multi-Grained Supervision for Image-Text Retrieval." In ICMR '22: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3512527.3531368.

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Ergul, Mustafa, and Aydin Alatan. "Depth is all you Need: Single-Stage Weakly Supervised Semantic Segmentation From Image-Level Supervision." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897161.

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